Design of Dynamic Prediction System for Electricity Heterogeneous Data Based on Deep Neural Network
Abstract
Electricity pricing and incentive mechanisms play an important role in consumer usage pattern development in a Smart Grid (SG) ecosystem. Variations in these patterns have a direct effect on the fluctuation in prices as well as the overall demand. For that reason, reliable and timely forecasting of electricity prices and consumption demand is necessary to ensure SG reliability, stability, and effective maintenance. With the proliferation of smart meters, sensors and monitoring systems, the SG environment is now providing huge volumes of heterogeneous data and big-data driven forecasting is a rapidly advancing area of research. Empowering the consumers of power with accurate knowledge of prices and demands allows the ability to manage the load efficiently, which could be one of the factors in improving energy efficiency and cutting down operational costs.